{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "import mindspore\n",
    "import mindspore.ops as ops\n",
    "import mindspore.nn as nn\n",
    "import mindspore.dataset as ds\n",
    "import mindspore.dataset.vision as vision\n",
    "import mindspore.dataset.transforms as transforms\n",
    "from d2l import mindspore as d2l\n",
    "mindspore.set_context(device_target='GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@save\n",
    "d2l.DATA_HUB['hotdog'] = (d2l.DATA_URL + 'hotdog.zip',\n",
    "                         'fba480ffa8aa7e0febbb511d181409f899b9baa5')\n",
    "\n",
    "data_dir = d2l.download_extract('hotdog')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_imgs = ds.ImageFolderDataset(os.path.join(data_dir, 'train'), shuffle=False, decode=True)\n",
    "test_imgs = ds.ImageFolderDataset(os.path.join(data_dir, 'test'), shuffle=False, decode=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 806.4x201.6 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hotdogs = []\n",
    "not_hotdogs = []\n",
    "for i, (image, label) in enumerate(train_imgs.create_tuple_iterator()):\n",
    "    if i < 8:\n",
    "        hotdogs.append(image)\n",
    "    elif i > train_imgs.get_dataset_size() - 9:\n",
    "        not_hotdogs.append(image)\n",
    "d2l.show_images(hotdogs + not_hotdogs, 2, 8, scale=1.4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalize = vision.Normalize(\n",
    "    [0.485 * 225, 0.456 * 225, 0.406 * 225], [0.229 * 225, 0.224 * 225, 0.225 * 225])\n",
    "\n",
    "train_augs = transforms.Compose([\n",
    "    vision.RandomResizedCrop(224),\n",
    "    vision.RandomHorizontalFlip(),\n",
    "    normalize,\n",
    "    vision.HWC2CHW()])\n",
    "\n",
    "test_augs = transforms.Compose([\n",
    "    vision.transforms.Resize([256, 256]),\n",
    "    vision.transforms.CenterCrop(224),\n",
    "    normalize,\n",
    "    vision.HWC2CHW()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WARNING] ME(4618:140498888794688,MainProcess):2023-02-15-19:03:43.644.476 [mindspore/common/api.py:824] 'mindspore.ms_class' will be deprecated and removed in a future version. Please use 'mindspore.jit_class' instead.\n",
      "[WARNING] ME(4618:140498888794688,MainProcess):2023-02-15-19:03:43.662.539 [mindspore/common/api.py:678] 'mindspore.ms_function' will be deprecated and removed in a future version. Please use 'mindspore.jit' instead.\n",
      "[WARNING] ME(4618:140498888794688,MainProcess):2023-02-15-19:03:43.663.985 [mindspore/common/api.py:678] 'mindspore.ms_function' will be deprecated and removed in a future version. Please use 'mindspore.jit' instead.\n"
     ]
    }
   ],
   "source": [
    "import mindcv\n",
    "\n",
    "pretrained_net = mindcv.create_model('resnet18', pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dense<input_channels=512, output_channels=1000, has_bias=True>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_net.classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter (name=classifier.weight, shape=(2, 512), dtype=Float32, requires_grad=True)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mindspore.common.initializer as initializer\n",
    "\n",
    "finetune_net = mindcv.create_model('resnet18', pretrained=True)\n",
    "finetune_net.classifier = nn.Dense(finetune_net.classifier.in_channels, 2)\n",
    "finetune_net.classifier.weight.set_data(\n",
    "    initializer.initializer(initializer.XavierNormal(), finetune_net.classifier.weight.shape, finetune_net.classifier.weight.dtype))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果param_group=True，输出层中的模型参数将使用十倍的学习率\n",
    "def train_fine_tuning(net, learning_rate, batch_size=128, num_epochs=5,\n",
    "                      param_group=True):\n",
    "    train_iter = ds.ImageFolderDataset(os.path.join(data_dir, 'train'), \n",
    "                                       shuffle=True, decode=True)\n",
    "    train_iter = train_iter.map(train_augs, input_columns=[\"image\"])\n",
    "    train_iter = train_iter.batch(batch_size=batch_size)\n",
    "    test_iter = ds.ImageFolderDataset(os.path.join(data_dir, 'test'), \n",
    "                                       shuffle=True, decode=True)\n",
    "    test_iter = test_iter.map(test_augs, input_columns=[\"image\"])\n",
    "    test_iter = test_iter.batch(batch_size=batch_size)\n",
    "    \n",
    "    loss = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "    if param_group:\n",
    "        params_1x = [param for param in net.get_parameters()\n",
    "             if param.name not in [\"classifier.weight\", \"classifier.bias\"]]\n",
    "        trainer = nn.SGD([{'params': params_1x},\n",
    "                        {'params': net.classifier.get_parameters(),\n",
    "                        'lr': learning_rate * 10}],\n",
    "                        learning_rate=learning_rate, weight_decay=0.001)\n",
    "    else:\n",
    "        trainer = nn.SGD(net.get_parameters(), learning_rate=learning_rate,\n",
    "                        weight_decay=0.001)\n",
    "    d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.205, train acc 0.918, test acc 0.931\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "train_fine_tuning(finetune_net, 5e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_4618/1160317576.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mscratch_net\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmindcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'resnet18'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mscratch_net\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinetune_net\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_channels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain_fine_tuning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscratch_net\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_group\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_4618/4282979854.py\u001b[0m in \u001b[0;36mtrain_fine_tuning\u001b[0;34m(net, learning_rate, batch_size, num_epochs, param_group)\u001b[0m\n\u001b[1;32m     22\u001b[0m         trainer = nn.SGD(net.get_parameters(), learning_rate=learning_rate,\n\u001b[1;32m     23\u001b[0m                         weight_decay=0.001)\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mtrain_ch13\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/tmp/ipykernel_4618/1242901079.py\u001b[0m in \u001b[0;36mtrain_ch13\u001b[0;34m(net, train_iter, test_iter, loss, trainer, num_epochs)\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m             l, acc = train_batch_ch13(\n\u001b[0;32m---> 36\u001b[0;31m                 train_step, features, labels) # , loss, trainer, devices\n\u001b[0m\u001b[1;32m     37\u001b[0m             \u001b[0mmetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_4618/1242901079.py\u001b[0m in \u001b[0;36mtrain_batch_ch13\u001b[0;34m(train_step, X, y)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_batch_ch13\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# , loss, trainer, devices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"用多GPU进行小批量训练\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mtrain_loss_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtrain_acc_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md2l\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_4618/1242901079.py\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(inputs, targets)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m         \u001b[0mtrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/composite/base.py\u001b[0m in \u001b[0;36mafter_grad\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    603\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_by_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m                     \u001b[0;32mdef\u001b[0m \u001b[0mafter_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m                         \u001b[0;32mreturn\u001b[0m \u001b[0mgrad_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m                     \u001b[0;32mdef\u001b[0m \u001b[0mafter_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/common/api.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*arg, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_convert_python_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/composite/base.py\u001b[0m in \u001b[0;36mafter_grad\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    580\u001b[0m             \u001b[0;34m@\u001b[0m\u001b[0m_wrap_func\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mafter_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pynative_forward_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    583\u001b[0m                 \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_position\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/composite/base.py\u001b[0m in \u001b[0;36m_pynative_forward_run\u001b[0;34m(self, fn, grad, args, kwargs)\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_flag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m                 \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m                 \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m                 \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/composite/base.py\u001b[0m in \u001b[0;36maux_fn\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0maux_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    547\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"When has_aux is True, origin fn requires more than one outputs.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_4618/1242901079.py\u001b[0m in \u001b[0;36mforward_fn\u001b[0;34m(inputs, targets)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/nn/cell.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    652\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m             \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 654\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_construct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    655\u001b[0m             \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/nn/cell.py\u001b[0m in \u001b[0;36m_run_construct\u001b[0;34m(self, cast_inputs, kwargs)\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shard_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_enable_forward_hook\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_forward_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindcv/models/resnet.py\u001b[0m in \u001b[0;36mconstruct\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindcv/models/resnet.py\u001b[0m in \u001b[0;36mforward_head\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward_head\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/nn/cell.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    652\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m             \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 654\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_construct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    655\u001b[0m             \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/nn/cell.py\u001b[0m in \u001b[0;36m_run_construct\u001b[0;34m(self, cast_inputs, kwargs)\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shard_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_enable_forward_hook\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_forward_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/nn/layer/basic.py\u001b[0m in \u001b[0;36mconstruct\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    766\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_shape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    769\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_bias\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/primitive.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mshould_elim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_run_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/primitive.py\u001b[0m in \u001b[0;36m_run_op\u001b[0;34m(obj, op_name, args)\u001b[0m\n\u001b[1;32m    883\u001b[0m         \u001b[0mstub_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_op_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_convert_stub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstub_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstub\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_run_op_sync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/common/api.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*arg, **kwargs)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_convert_python_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/ops/primitive.py\u001b[0m in \u001b[0;36m_run_op_sync\u001b[0;34m(obj, op_name, args)\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_op_sync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m     \u001b[0;34m\"\"\"Single op execution function in synchronous mode.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pynative_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal_run_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/mindspore/common/api.py\u001b[0m in \u001b[0;36mreal_run_op\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1021\u001b[0m             \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mof\u001b[0m \u001b[0mrun\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \"\"\"\n\u001b[0;32m-> 1023\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_executor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal_run_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrun_op_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "scratch_net = mindcv.create_model('resnet18')\n",
    "scratch_net.classifier = nn.Dense(finetune_net.classifier.in_channels, 2)\n",
    "train_fine_tuning(scratch_net, 5e-4, param_group=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in finetune_net.get_parameters():\n",
    "    param = ops.stop_gradient(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter (name=classifier.weight, shape=(2, 512), dtype=Float32, requires_grad=True)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight = pretrained_net.classifier.weight\n",
    "weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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